INTRODUCTION
Research background
This section investigates the growing market of luxury brands and products worldwide, especially in Asian countries It also gives an overview of battle against counterfeiting of luxury brands.
The luxury products market is experiencing rapid expansion, with researchers acknowledging its swift growth despite varying estimates of market size (Heine & Phan, 2011; Ho et al., 2012; Truong et al., 2008) This surge can be attributed to two key factors: an improving economy leading to lower unemployment rates and production costs, and the inclusion of lower-class consumers in the luxury market According to Truong et al (2008), these consumers are motivated to purchase luxury items for reasons such as imitation of the affluent, self-reward, and appreciation for quality This phenomenon has given rise to the concept of "masstige," blending mass market appeal with prestige (Truong et al., 2008) Additionally, Cavender and Kincade (2013) highlight that globalization has lowered entry barriers, allowing more companies to enter the luxury market, which was once dominated by a select few with advanced resources and technologies.
The luxury products market is rapidly expanding beyond Western nations, with significant growth observed in Asian countries such as China, India, and Korea This trend highlights the increasing demand for research on luxury products in these regions, underscoring the importance of understanding consumer behavior and market dynamics in the Asian luxury sector.
Rapid growth presents significant challenges for luxury brands, as it raises concerns about maintaining a balance between brand expansion and exclusivity According to Hennigs et al (2013), as luxury brands become more accessible to the masses, their perceived rarity diminishes, undermining their status Additionally, brand managers face the dual challenge of preserving quality while combating the increasing prevalence of counterfeits, which threaten revenue and profit margins The rising production costs of counterfeit goods exacerbate the issue, flooding the market and further eroding the luxury perception of these brands.
Counterfeiting poses a significant challenge to branding, particularly for luxury brands Hardy (2014) projected that the counterfeit market would reach a value of $1.7 million in 2015, encompassing a wide range of products from casual items to high-end goods Despite ongoing efforts to enforce intellectual property laws and combat counterfeiting, it remains a pervasive global issue, notably in regions such as China, Sub-Saharan Africa, Southeast Asia, and the Western Balkans (Meiring, 2014; Mijatovic et al., 2014; Phau & Teah, 2009; Vachanavuttivong et al., 2014).
Luxury brands face significant threats from counterfeits, which are produced at low costs and can lead to sharp revenue declines, putting financial pressure on companies and creating high entry barriers in the industry (Phau & Teah, 2009; Staake et al., 2009) Consumers of highly-counterfeited brands often perceive these brands as less attractive, and many intentionally opt for counterfeit products as a cheaper alternative, despite the associated risks and the efforts brands make to differentiate their authentic offerings (Staake et al., 2009; Perez et al., 2010) Consequently, understanding the motivations of counterfeit consumers is crucial for mitigating the impact of luxury brand counterfeiting.
Existing studies on counterfeiting
A review by Staake et al (2009) highlights that while numerous studies have explored supply-side factors influencing consumers' intentions to purchase counterfeits, demand-side reasons remain under-researched Many scholars, such as Penz & Stürttinger (2005), attribute counterfeit purchases primarily to deceitful sellers However, evidence from Perez et al (2010) indicates that consumers often knowingly choose to buy counterfeits, underscoring the need for a more thorough examination of demand-side factors.
Research on the psychological factors influencing intentional counterfeit purchasing, particularly the value-expressive and social-adjustive aspects, remains limited These aspects, which relate to self-expression and self-presentation, are crucial to understanding luxury brand values (Nia & Zaichkowsky, 2000) While Wilcox, Kim, and Sen (2009) have explored the functions of value-expressive and social-adjustive roles, their study does not delve into how these functions significantly affect the intention to purchase counterfeit goods.
While previous research has explored the psychological aspects of counterfeit purchasing, there is a lack of studies examining the moderating effects of key factors such as product involvement and product knowledge, which are crucial in predicting consumer behavior (Bian & Moutinho, 2011) Bian and Moutinho (2011) emphasize that "product involvement is a central framework, vital to understanding consumer decision-making behavior and associated communications" (p 195) Therefore, it is essential to investigate the influence of these two significant concepts on consumer behavior, particularly in the context of counterfeit purchase intentions.
Research objectives
This research investigates the psychological factors influencing counterfeit purchase intentions, focusing on the value-expressive and social-adjustive functions Additionally, it explores how product involvement moderates the relationships between these functions and luxury counterfeit purchase intentions, as well as the role of product knowledge in moderating the connection between social-adjustive function and luxury counterfeit purchase intention.
1 To what extent do value-expressive function and social-adjustive function influence counterfeit purchase intention in the cases of luxury brands?
2 To what extent does product involvement moderate the relationships among value-expressive function, social-adjustive function, and luxury counterfeit purchase intention?
3 To what extent does product knowledge moderate the relationship between social-adjustive function and luxury counterfeit purchase intention?
Scope of the study
This study examines how psychological factors influence the intention to purchase counterfeit products, while also considering the moderating roles of consumer product involvement and product knowledge The research is specifically focused on consumers in Ho Chi Minh City and is limited to the fashion luxury sector due to resource constraints.
Research significance
This research enhances the understanding of the luxury market's challenges with counterfeiting, particularly in Vietnam Additionally, the insights gained will assist luxury brand managers in strategically selecting effective marketing combinations to diminish customers' intentions to purchase counterfeit goods.
Research structure
This paper contains five chapters, including introduction, literature review, research methodology, data analysis, and discussion and conclusion Specifically:
Chapter 1 presents the struggles luxury brands have to face against counterfeits, as well as points out previous important works on the issues This chapter also figures out specific research objectives, research questions and research scope The structure of this paper is also fully presented.
Chapter 2 defines clearly the constructs (value-expressive function, social-adjustive function, product knowledge, product involvement and counterfeit purchase intention) as well as other definitions (counterfeits, luxury brands) and digs deeper into previous works. Hypotheses and conceptual model are also developed in this chapter with detail justifications.
Chapter 3 describes the method to conduct the research, including data collection(method and measurement) and data analysis Details of the process of building the questionnaire, as well as the collection methods and tools of testing the data reliability and validity are also given in this chapter.
Chapter 4 includes the results and findings inferred from the collected data. Measurement and hypothesis tests are run and presented in details, using Statistical Package for Social Science (SPSS) 16.0 and AMOS 20.0 of International Business Machines Corporation (IBM) The results of the tests are also summarized for further discussion in Chapter 5.
Chapter 5 discusses further the results and findings from Chapter 4 to draw out managerial implications applicable in real life Limitations of this paper are also clearly stated to suggest further researches in the future.
LITERATURE REVIEW
Luxury brands
The definition of luxury is subjective and varies across different contexts, making it challenging to distinguish luxury brands from non-luxury ones (Wiedmann, Hennigs, & Siebels, 2007; Vigneron & Johnson, 2004) Common characteristics of luxury brands include rarity, high quality, elevated price, and significant self-expressive and social meaning (Miller & Mills, 2012; Vigneron & Johnson, 2004; Wiedmann et al., 2007) Additionally, Nia and Zaichkowsky (2000) emphasize that the key difference between luxury and non-luxury brands lies in the psychological versus functional value, with luxury brands offering a higher psychological value that greatly influences purchasing decisions.
In this study, luxury brands are characterized as those that fulfill consumers' psychological needs for self-expression and self-presentation These brands are distinguished by their rarity, uniqueness, high quality, and premium pricing.
Counterfeits
Counterfeits are products that falsely imitate the trademarks, logos, names, or designs of legitimate brands to exploit their market value (Bian & Moutinho, 2009; Phau & Teah, 2009; Staake et al, 2009; Wilcox et al, 2009) While traditionally associated with low quality, the standard of counterfeit goods has been improving over time, and in some instances, they now match the quality of authentic products (Wilcox et al, 2009).
Counterfeits are classified into two main categories: deceptive and non-deceptive Deceptive counterfeits are intentionally designed to mislead consumers, resulting in unknowing purchases In contrast, non-deceptive counterfeits are easily recognizable by consumers due to noticeable differences in appearance, trademarks, or distribution channels, leading to intentional purchases This research specifically examines non-deceptive counterfeits.
Counterfeit purchase intention
Behavioral intention refers to an individual's willingness and effort to engage in a specific behavior (Ajzen, 1991) In the context of luxury brands, counterfeit purchase intention is defined as the readiness and determination to buy counterfeit products According to Ajzen, behavioral intention serves as a reliable predictor of actual behavior, making counterfeit purchase intention an effective indicator of the likelihood of engaging in counterfeit purchases.
The Theory of Planned Behavior (Ajzen, 1991) posits that individuals cannot intend to perform an action without having control over it Consequently, the intention to purchase counterfeit products is not applicable when dealing with deceptive counterfeits This research specifically examines the intention to buy non-deceptive counterfeits, highlighting the importance of consumer control in purchasing behavior.
Value-expressive and social-adjustive function
Research by Grewal, Mehta, and Karrdes (2004), Katz (1960), and Wilcox et al (2009) highlights that individuals develop attitudes towards products to fulfill various psychological functions, such as facilitating decision-making, maximizing value, expressing personal values, and enhancing social interactions Additionally, studies utilizing the theory of planned behavior indicate that attitudes towards counterfeit purchasing significantly influence the intention to buy counterfeit goods (Chiu, Lee, & Won, 2014; Koklic, 2011; Phau, Teah, & Lee, 2009) Consequently, the psychological functions associated with products can impact consumers' intentions to purchase counterfeits.
This research aims to investigate the social-side functions of the luxury products only—which mean value-expressive function and social-adjustive function Wilcox et al
According to research from 2009, consumers motivated by value-expressive functions tend to make purchases to convey their values, beliefs, or personalities, while those driven by social-adjustive functions buy products to fit into social contexts Essentially, value-expressive functions fulfill the need for self-expression, addressing personal self-judgments, whereas social-adjustive functions cater to the need for self-presentation, focusing on social judgments In this study, value-expressive function is defined as the aspect of a product that enables consumers to communicate their values, while social-adjustive function refers to the aspect that helps consumers integrate into social situations and interact with others.
Research indicates that the value-expressive function significantly impacts consumer decisions regarding counterfeit purchases, with Wiedmann, Hennigs, and Klarmann (2012) arguing that this influence is predominantly negative Consumers motivated by value-expressive factors often experience diminished satisfaction when buying counterfeits, as they are acutely aware of the differences between genuine products and replicas Supporting this view, respondents in a study by Perez et al (2010) expressed discomfort with counterfeit purchases, highlighting a sentiment shared by one participant, Cristina, who remarked, "You don’t feel the same way that you do with the original; you’re always going to know that" (Perez et al., 2010, p 226).
The social adjustive function significantly influences the intention to purchase counterfeit products (Wiedmann et al., 2012) Research by Perez et al (2010) indicates that super-non-deceptive counterfeits—those that are nearly indistinguishable from authentic items—offer similar social benefits, reinforcing their appeal among consumers.
Consumers tend to purchase counterfeits, especially those of trendy brands—brands containing high level of social-adjustive function (Perez et al, 2010) Hence, our hypotheses are stated as follows:
H1: Value-expressive function has a negative effect on counterfeit purchase intention.
H2: Social-adjustive function has a positive effect on counterfeit purchase intention.
Product involvement
Product involvement refers to the significance a consumer places on a specific product category based on how well it satisfies their needs or values When consumers perceive a product as relevant to their lives, their engagement and response to that product increase, resulting in higher levels of product involvement.
Product involvement encompasses two key aspects: its significance in a consumer's life and the consumer's interest or enjoyment derived from it As highlighted by McQuarrie & Munson (1992), Vaughn (1986), and Zaichkowsky (1987), greater product involvement leads to increased benefits for consumers when selecting the right product Consequently, consumers are more likely to invest additional effort in choosing high-involvement products, as noted by Bian & Moutinho (2011).
High-involvement products carry a significant risk of making poor choices, which diminishes the perceived advantages of counterfeit alternatives for consumers Additionally, the critical nature of these products can intensify feelings of self-deception regarding the intention to purchase counterfeits.
Consumers driven by value-expressive functions prioritize quality-related issues, particularly when it comes to counterfeits (Wilcox et al., 2009) As product involvement increases, the significance of the product to the consumer grows, making quality concerns more pressing compared to low-involvement products.
According to Bian and Moutinho (2011), high product involvement leads consumers to carefully evaluate their purchases, considering the social benefits of genuine products over counterfeits Additionally, the potential risk of being caught using counterfeit items is heightened with high-involvement products, diminishing the allure of counterfeit social benefits.
Hence, it can be proposed that:
H3: High level of product involvement strengthens the relationship between value- expressive function and counterfeit purchase intention.
H4: High level of product involvement weakens the relationship between social- adjustive function and counterfeit purchase intention.
Product knowledge
According to Marks and Olson (1981), product knowledge refers to the information about a product that is stored in an individual's memory, which can be gathered either directly or indirectly They also indicate that consumers with a high level of product knowledge engage in more complex decision-making processes during purchases, as they have access to a greater amount of information to evaluate.
Consumers with a high level of product knowledge are more adept at recognizing low-quality counterfeits, allowing them to differentiate between fake and genuine products (Bian & Moutinho, 2011) This awareness heightens their perception of the risks associated with using counterfeits, as they understand the potential consequences of being discovered Consequently, when the likelihood of being exposed is significant, the appeal of self-presentation diminishes, prompting these informed consumers to opt for authentic products instead (Perez et al, 2010).
H5: High level of product knowledge weakens the relationship between social- adjustive function and counterfeit purchase intention.
Summary
This chapter defines key concepts such as luxury, counterfeit, counterfeit purchase intention, value-expressive function, social-adjustive function, product involvement, and product knowledge, with a summary of these definitions provided in Table 2.1.
Luxury brands Brands bought mainly to serve consumers‘ psychological needs—specifically self-expressing and self-presenting—that are rare or unique, high- quality and highly priced
Miller & Mills (2012); Nia & Zaichkowsky (2000); Vigneron & Johnson (2004); Wiedmann et al (2007).
Counterfeits Products that bear a fake or indistinguishable trademark, logo, name or design of another product, in order to illegally take advantage of the brand value of the real product
The willingness and the effort of an individual to purchase counterfeits—in this case counterfeits of luxury brands
The function of a product that helps consumers to communicate or express their values.
The function of a product that helps consumers to fit in social situations, present themselves and interact with other people.
Product The perceived level of importance of a particular Bian & Moutinho (2011); involvement product category in consumer‘s life, because it meets the needs or values of the consumer.
Information relating to the product stored in the memory.
The relationships among concepts are also investigated to form the following hypotheses as in Table 2.2
H1 Value-expressive function has a negative effect on counterfeit purchase intention.
Perez et al (2010); Wiedmann et al (2012).
H2 Social-adjustive function has a positive effect on counterfeit purchase intention.
Perez et al (2010); Wiedmann et al (2012).
H3 High level of product involvement strengthens the relationship between value-expressive function and counterfeit purchase intention.
Bian & Moutinho (2011); Perez et al (2010); Snyder and DeBono
(1985, as cited in Wilcox et al, 2009);
H4 High level of product involvement weakens the relationship between social-adjustive function and counterfeit purchase intention.
Bian & Moutinho (2011); Perez et al (2010); Wiedmann et al (2012).
H5 High level of product knowledge weakens the relationship between social-adjustive function and counterfeit purchase intention.
Bian & Moutinho (2011); Perez et al (2010).
The conceptual model of this research is presented below:
Draft questionnaire Cronbach‘s alpha test
In-depth interview (n=5) Exploratory factor analysis
Final questionnaire Hierarchical multiple regression
RESEARCH METHODOLOGY
Data collection
The data collecting process included two separate phases: a pilot study and a main
The pilot study involved conducting five in-depth interviews with selected customers To enhance clarity, the questionnaire was translated into Vietnamese, and participants provided feedback to refine the questions, ensuring they were easily understood.
Following the pilot study, a larger-scale main study was conducted utilizing a convenient sampling method Respondents, specifically every fifth customer entering Diamond Plaza in District 1, Ho Chi Minh City, Vietnam, were invited to complete a revised self-administered questionnaire in Vietnamese The data gathered from this main study was then analyzed to assess both the reliability and validity of the proposed model.
Measurement
The questionnaire contained 25 items measuring five constructs: value-expressive function, social-adjustive function, counterfeit purchase intention, product involvement and product knowledge.
The study assessed both the value-expressive and social-adjustive functions using four items for each category These items, derived from Wilcox et al (2009), utilized a seven-point Likert scale, where participants rated their agreement from 1, indicating "completely disagree," to 7, representing "completely agree."
The intention to purchase counterfeit items was assessed using three items based on the framework established by Hung et al (2011) These items were formulated as seven-point Likert-scale questions, with responses ranging from 1, indicating "completely disagree," to 7, signifying "completely agree."
The study utilized the Revised Personal Involvement Inventory by McQuarrie and Munson (1992) to assess product involvement, employing a widely recognized scale comprising 10 items Each item featured 7-point bipolar evaluative adjective pairs, ensuring a comprehensive measurement of consumer engagement with the product.
Product knowledge construct was measured using the four items developed by Smith and Park (1992) The four items were seven-point Likert-type questions, ranging from 1 –
―completely disagree‖ to 7 – ―completely agree.‖
Code Item Reference Value-expressive function
Luxury brand reflects the kind of person I see myself to be.
Luxury brand helps me communicate my self-identity Wilcox et al
VE3 Luxury brand helps me express myself (2009)
VE4 Luxury brand helps me define myself.
Luxury brand is a symbol of social status.
Luxury brand helps me fit into important social situations Wilcox et al
SA3 I like to be seen wearing/using luxury brand (2009)
SA4 I enjoy it when people know I am wearing/using a luxury brand.
I feel very knowledgeable about fashion products.
If a friend asked me about fashion products, I could give them advice about different brands.
PK3 If I had to purchase fashion products today, I would need to gather very little information in order to make a wise decision (1992)
PK4 I feel very confident about my ability to tell the difference in quality among different brands of fashion products.
I consider fashion products to be…
PI3 Means nothing to me/Means a lot to me McQuarrie and
The final measurement scales are presented as in Table 3.1.
PI6 Doesn‘t matter/Matter to me
PI10 Of no concern/Of concern to me
CP1 I have strong possibility to purchase counterfeits of fashion-related luxury product.
Hung et al (2011) CP2 I‘m likely to purchase counterfeits of fashion-related luxury product.
CP3 I have high intention to purchase counterfeits of fashion-related luxury product.
Participants were instructed to consider a luxury fashion brand prior to responding to the survey questions The questionnaire was translated into Vietnamese and refined during a pilot study before its implementation in the main research.
Luxury brands
The perception of luxury varies among individuals, as highlighted by Wiedmann et al (2007) To explore this concept, respondents were presented with well-known luxury brands such as Rolex, Gucci, and Louis Vuitton This approach aims to deepen the understanding of luxury, particularly since fashion items are prime targets for counterfeiters, according to Eisend & Schuchert-Güler.
2006), respondents were asked to consider fashion-related luxury products that they have bought, such as clothes, purse, watch, shoes before answering the questions.
Measurement validation
The convergent validity was assessed using three key methods: construct reliability, item reliability, and average variance extracted Construct reliability was evaluated through Cronbach's alpha and composite reliability scores, following the guidelines established by Nunnally and Bernstein.
To ensure reliability in research, Cronbach's alpha for each group should exceed 0.70, as noted by Cronbach (1994) Molina, Montes, and Ruiz-Moreno (2007) also recommend that the composite reliability score should be above 0.70 According to Chin (1998), item loadings must not fall below 0.60 to meet item reliability standards Additionally, Fornell and Larcker (1981) suggest that the average variance extracted for constructs should be 0.50 or higher.
Discriminant validity was assessed through the analysis of cross-loadings and the square root of the average variance extracted According to the guidelines established by Chin (1998) and Fornell and Larcker (1981), a model demonstrates discriminant validity when the square root of the average variance extracted for each construct is greater than the correlations between that construct and any other constructs.
Exploratory factor analysis was conducted to gather data on item loadings and cross-loadings, following the guidelines set by Pallant (2007), which recommend a sample size of at least 150 and a case-per-item ratio of 5:1 or higher (Comfrey & Lee, 1992; Hair et al., 1998) Given that the questionnaire contained 25 items, a minimum of 150 responses was necessary to effectively perform the exploratory factor analysis.
(5 x 25 = 125, lower than the 150 criterion) Furthermore, Pallant (2007) also suggests that the Kaiser-Meyer-Olkin value should not be lower than 0.6, and the Bartlett‘s test of sphericity should be significant (p < 0.05).
To evaluate model fit, confirmatory factor analysis was conducted using specific cut-off criteria: a χ²/df ratio of 3:1 or lower, a Comparative Fit Index (CFI) of 0.95 or higher, a Root Mean Square Error of Approximation (RMSEA) of 0.06 or less, and a Standardized Root Mean Square Residual (SRMR) of 0.08 or below.
To figure out any multicollinearity issues that might occurred, Pearson correlation analysis was conducted According to Field (2005), all correlation should be below 0.8 to avoid multicollinearity. scales.
After measurement validation, hypotheses tests were conducted using the modified
Hypotheses tests
To examine the moderation effects, product involvement and product knowledge were categorized using the median-split method This resulted in two levels for product involvement: low and high, as well as two levels for product knowledge: low and high.
Particularly, to test the hypotheses proposed in Chapter 2, the following hierarchical multiple regression models were used:
Model A was employed to examine the impact of value-expressive function on counterfeit purchase intention (H1) and to analyze the moderating effect of product involvement on this relationship (H3) through hierarchical multiple regression The analysis consisted of three steps: the first step included only the value-expressive function, the second incorporated product involvement, and the third added the interaction term of value-expressive function and product involvement The equation for Model A is represented as follows: y = β0 + β1x1 + β2x3 + β3x1x3 + ε1, where y denotes counterfeit purchase intention, x1 represents value-expressive function, x3 indicates product involvement (0 for low and 1 for high involvement), and ε1 signifies random error.
Model B was employed to examine the impact of social-adjustive function on counterfeit purchase intention (H2) and to explore the moderating role of product involvement in this relationship (H4) through hierarchical multiple regression analysis The analysis was conducted in three steps: the first step assessed the effect of social-adjustive function alone, the second step incorporated product involvement, and the final step included the interaction term between social-adjustive function and product involvement The equation representing Model B is as follows: y = β0' + β1'x2 + β2'x3 + β3'x2x3 + ε2, where y denotes counterfeit purchase intention, x2 represents social-adjustive function, x3 indicates product involvement (with 0 for low and 1 for high involvement), and ε2 signifies random error.
In this study, Model C was employed to examine how product knowledge moderates the relationship between social-adjustive function and counterfeit purchase intention (H5) through hierarchical multiple regression The analysis consisted of two steps: the first step incorporated social-adjustive function and product knowledge, while the second step introduced the interaction term of social-adjustive function and product knowledge The equation representing Model C is as follows: y = β0 + β1x2 + β2x4 + β3x2x4 + ε3, where y denotes counterfeit purchase intention, x2 represents social-adjustive function, x4 indicates product knowledge (with 0 signifying low product knowledge and 1 indicating high product knowledge), and ε3 refers to random error.
Moreover, in order to apply multiple regression analysis, according to Tabachnick and Fidell (2001), the minimum sample size should be: n > 50 + 8m where: n: minimum sample size; m: number of predictor variables. be 82.
Since the number of predictor variables was four, the minimum sample size should
Summary
The data collection process involved two key phases: an initial pilot study to identify any issues with questionnaire comprehension, followed by a main survey to gather data Participants were instructed to reflect on a fashion-related product they perceived as luxurious while responding to the questionnaire, given the subjective nature of the term "luxury."
The data would go through measurement validation The criteria in measurement validation were specified in details in Table 3.2.
Table 3.2: Summary of research criteria
Cronbach‘s alpha of each group Nunnally and Bernstein
Composite reliability Molina et al (2007) ≥ 0.70
Average variance extracted Fornell and Larcker
Discriminant validity Chin (1998); Fornell and Larcker (1981)
Square-root of the average variance extracted of each construct > correlation between that construct and any other constructs.
Comfrey and Lee (1992); Hair et al (1998) case-per-item ratio ≥ 5:1 (or ≥ 125 in this paper)
(2001) n > 50 + 8m (or ≥ 82 in this paper) Kaiser-Meyer-Olkin test Pallant (2007) ≥ 0.60
Bartlett‘s test of sphericity Pallant (2007) p < 0.05 χ 2 /df Kline (2005) ≤ 3:1
After measurement validation tests, final data were used to test the hypotheses, Hierarchical multiple regression was employed in this stage.
DATA ANALYSIS
Pilot study
The questionnaire was translated into Vietnamese and administered to five respondents Each respondent was presented with the items individually and asked to articulate their understanding of each one The findings are detailed below.
Table 4.1: In-depth interview results
PK3 Mistakenly interpret this item as ―need to gather very little information‖ due to
√ √ √ Mistakenly interpret this item as ―need to gather very little information‖ due to
―low interest in fashion‖ ―low interest in fashion‖
PI2 √ √ Suggest stating more specifically
√ Fully and correctly understand the item.
Most of the items were understood correctly, except PK3 and PI2 Respondent A and Respondent E mistakenly interpreted PK3 as ―need to gather very little information‖ due to
The misunderstanding regarding the low interest in fashion stemmed from respondents overlooking the word "wise." This led to the interpretation that minimal information would be needed to make a purchasing decision on fashion products However, since the confusion was attributed to the respondents' carelessness, no changes were made to the item.
All respondents comprehended Item PI2 well, but Respondent C recommended that the question be more detailed by explicitly indicating what is considered relevant or irrelevant Despite this suggestion, the consensus among the respondents was that the existing specification was sufficient, leading to the decision not to modify the item.
In summary, the items were not changed after the pilot study, and were used in the main survey.
Data collection result and demographics
Finally 248 people agreed to take part in the research, and 201 usable responses were collected The other 47 responses were eliminated because they were not completed or not seriously answered.
The analysis revealed key demographics, focusing on gender and past purchasing behavior regarding luxury products The gender distribution was balanced, while a significant majority of respondents had previously purchased luxury items This trend can be attributed to the specific characteristics of the location where the data was collected.
Have not bought luxury products Have bought luxury products Total Female 34 (16.92%) 74 (36.82%) 108 (53.73%)
Measurement validation
The initial analysis focused on assessing the reliability and validity of the scales using several statistical methods These included Cronbach's alpha, exploratory factor analysis, confirmatory factor analysis, composite reliability scores, average variance extracted scores, divergent reliability, and Pearson correlation.
In the measurement validation process outlined in Chapter 3, the Cronbach's alpha test was conducted to identify and remove any problematic items, thereby enhancing construct reliability The outcomes of the initial Cronbach's alpha test are presented as follows.
Value-expressive function VE1,VE2, VE3, VE4 880
Social-adjustive function SA1, SA2, SA3, SA4 835
Product knowledge PK1, PK2, PK3, PK4 817
Product involvement PI1, PI2, PI3, PI4, PI5, PI6,
Counterfeit purchase intention CP1, CP2, CP3 896
All Cronbach‘s alpha scores were above 7 However, the item-total statistics tables
—particularly those of product knowledge scale and of counterfeit purchase intention scale
To enhance the Cronbach's alpha score for product knowledge, it was recommended to delete item PK3, which would raise the score from 817 to 852, an increase of 035 With three remaining items, the scale maintains sufficient reliability, as noted by Hinkin (1995) Consequently, item PK3 was removed from the product knowledge scale to improve its overall effectiveness.
Table 4.4: Item-total statistics for product knowledge scale
Scale Mean if Item Deleted
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted
To enhance the Cronbach's alpha score for counterfeit purchase intention, it was proposed to eliminate item CP3, which would increase the score from 896 to 952 However, Hinkin (1995) notes that constructs with a limited number of items tend to be unreliable The three-item scale's Cronbach's alpha of 896 is close to the 9 threshold, deemed excellent according to George & Mallery (2003) Additionally, CP3 exhibited a strong item-total correlation of 674 with the counterfeit purchase intention scale Consequently, CP3 was retained in the scale.
Table 4.5: Item-total statistics for counterfeit purchase intention scale
Scale Mean if Item Deleted
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted
The recalculation of Cronbach's alphas for each modified group revealed an improvement in the scale measuring product knowledge after the removal of PK3 All Cronbach's alphas exceeded the acceptable threshold of 7, with the social-adjustive function scale achieving the lowest alpha of 835, thereby meeting the established criteria for reliability.
Table 4.6: Cronbach‘s alphas after item reduction
Construct Item Corrected Item-Total
Correlation Cronbach's Alpha if Item
As stated in Chapter 3, the minimum number of responses to run exploratory factor analysis should be 150 or above The number of qualified responses was 201, hence passed the minimum threshold.
The Kaiser-Meyer-Olkin measure of sampling adequacy yielded a score of 868, exceeding the 6 threshold, while Bartlett’s Test of Sphericity showed a highly significant result (p = 000) These findings confirm the validity of the exploratory factor analysis conducted.
Table 4.7: KMO and Bartlett‘s test
Kaiser-Meyer-Olkin Measure of Sampling
Bartlett's Test of Sphericity Approx Chi-Square df Sig.
Matsunaga (2010) suggests that Promax rotation is preferable to Varimax rotation in social research Consequently, Promax rotation with Kaiser Normalization and k=4 was utilized, resulting in all loadings exceeding 6, which indicated that no items needed to be removed.
The loadings are given in Table 4.8.
The loadings table indicates that the difference between the highest and second-highest loadings for all items exceeded 2, confirming the absence of any cross-loaded items.
Table 4.8: Promax rotation with Kaiser Normalization, k=4
Extraction Method: Principal Component Analysis 8 Rotation Method: Promax with Kaiser Normalization
Loadings lower than 5 is not showed.
A confirmatory factor analysis was conducted to evaluate the model fit, yielding acceptable values (χ²/df = 2.172 and SRMR = 0597) However, the CFI of 910 and RMSEA of 077 did not meet the criteria established in Chapter 3, indicating that the model requires further improvement.
M.I Par Change M.I Par Change e24 < > CPI 5.968 305 e15 < > CPI 9.323 -.411 e24 < > VEF 5.743 193 e15 < > e24 11.192 -.284 e22 < > CPI 5.305 -.263 e15 < > e21 7.233 -.185 e22 < > PK 11.132 -.279 e15 < > e20 19.3 348 e22 < > e23 64.906 575 e15 < > e19 6.129 -.194 e21 < > CPI 4.828 223 e15 < > e17 38.327 547 e21 < > PK 4.022 -.149 e13 < > e23 4.053 106 e21 < > e24 13.767 234 e12 < > e23 7.567 -.152 e21 < > e23 6.871 -.165 e11 < > SAF 7.617 223 e20 < > PK 4.314 177 e11 < > e14 5.918 294 e19 < > e24 7.068 192 e11 < > e13 4.019 -.131 e19 < > e23 7.997 -.203 e10 < > e22 6.632 -.191 e19 < > e21 25.801 296 e10 < > e18 5.924 192 e19 < > e20 8.571 -.198 e10 < > e16 5.667 201 e18 < > PK 10.256 285 e10 < > e13 5.203 124 e18 < > e22 9.155 -.211 e9 < > e20 14.065 273 e17 < > CPI 4.37 -.271 e8 < > e24 5.803 -.202 e17 < > PK 4.88 21 e8 < > e11 6.026 253 e17 < > e24 11.867 -.281 e7 < > VEF 4.45 -.154 e17 < > e21 10.853 -.218 e6 < > e19 6.051 191 e17 < > e20 7.691 212 e5 < > e24 4.057 203 e17 < > e19 5.405 -.175 e5 < > e19 5.28 -.213 e16 < > PK 4.751 208 e5 < > e8 4.1 -.215 e16 < > e22 7.889 -.211 e3 < > SAF 4.652 138 e16 < > e18 9.006 239 e1 < > e5 9.682 332
The modification indices covariances table indicated potential improvements to the model by introducing correlation lines between specific error terms: e22 (error of PI8) with e23 (error of PI9), e19 (error of PI5) with e21 (error of PI7), and e15 (error of PI1) with e17 (error of PI3) This recommendation is justified, as these errors pertain to items assessing the same construct.
Therefore the model was modified by adding correlation lines between e22 and e23, e19 and e21, e15 and e17 Confirmatory factor analysis was run again on the modified model.
After modifications, the model fit figures showed significant improvement, meeting all established cut-off criteria: χ²/df = 1.637 (less than 3:1), SRMR = 0556 (below 08), CFI = 952 (above 95), and RMSEA = 056 (under 06) The confirmatory factor analysis model demonstrated a good fit without the need to delete any items.
Figure 4.1: Modified confirmatory factor analysis model
4.3.4 Composite reliability and average variance extracted
The analysis revealed that both composite reliability and average variance extracted scores were computed from the standardized regression weighted scores of the modified model Notably, all composite reliability scores exceeded 7, while all average variance extracted scores surpassed 5, thereby meeting the criteria established in Chapter 3.
Table 4.10: Composite score and average variance extracted
Construct Item Composite score Average variance extracted
Value-expressive function VE1,VE2, VE3, VE4 881 649
Social-adjustive function SA1, SA2, SA3, SA4 841 573
Product-knowledge PK1, PK2, PK4 857 668
Product-involvement PI1, PI2, PI3, PI4, PI5,
PI6, PI7, PI8, PI9, PI10 929 570
Counterfeit purchase intention CP1, CP2, CP3 906 765
4.3.5 Divergent reliability and Pearson correlation
The correlation scores and the square roots of the average variance extracted (AVE) scores were calculated using a unit-weighted method prior to conducting the Pearson correlation test The square roots of the AVE scores for each construct are highlighted in bold-italic, demonstrating that all AVE square roots surpass the correlations with other constructs, thus confirming the divergent reliability of the constructs.
To prevent multicollinearity issues, it is essential that all Pearson correlations remain below 8 (Field, 2005) The data presented in the table indicates that all correlations met this requirement, with the highest correlation being 496 between the value-expressive function and the social-adjustive function This confirms the absence of multicollinearity issues, ensuring the data is suitable for testing the hypotheses in the subsequent analysis.
Table 4.11: Square root of average variance extracted and correlations
VE SA PK PI CP
*** Correlation is significant at the 001 level (2-tailed).
In summary, the scales were adjusted as below, with all the items retained, except item PK3:
Construct Item Number of items
Value-expressive function VE1,VE2, VE3, VE4 4
Social-adjustive function SA1, SA2, SA3, SA4 4
Product-knowledge PK1, PK2, PK4 3
Product-involvement PI1, PI2, PI3, PI4, PI5,
PI6, PI7, PI8, PI9, PI10 10
Counterfeit purchase intention CP1, CP2, CP3 3
For a better view of the results of the measurement validation process, the status of each criterion is summarized below:
Table 4.13: Summary of research criteria results
Tests Reference(s) Criteria Scores Status
Cronbach‘s alpha of each group Nunnally and
Composite reliability Molina et al (2007) ≥ 70 lowest
Average variance extracted Fornell and Larcker
Discriminant validity Chin (1998); Fornell and Larcker (1981)
Square-root of the average variance extracted of each construct > correlation between that construct and any other constructs. qualified
Comfrey and Lee (1992); Hair et al (1998) case-per-item ratio ≥ 5:1 (or ≥ 125 in this paper)
Bartlett‘s test of sphericity Pallant (2007) p < 05 000 qualified χ 2 /df Kline (2005) ≤ 3:1 1.637 qualified
CFI Hu and Bentler (1999) ≥ 95 952 qualified
RMSEA Hu and Bentler (1999) ≤ 06 056 qualified
SRMR Hu and Bentler (1999) ≤ 08 0556 qualified
After the measurement validation process, hypotheses tests were conducted based on the modified scales.
Hypotheses tests
The number of observations was 201, much higher than the minimum sample size proposed to conduct multiple regression tests Therefore, the results of the tests below would be valid.
In Chapter 3, product involvement was categorized into low and high levels using the median-split method, with coding designated as 0 for low and 1 for high involvement Similarly, product knowledge was divided into low and high categories, also using the median-split method, and coded in the same manner To test the hypotheses, three hierarchical multiple regression models were utilized, as detailed in Chapter 3.
Model A was utilized to examine the influence of the value-expressive function on the intention to purchase counterfeit products (H1), along with the moderating effect of product involvement on this relationship (H3) The findings from this regression analysis are presented in Table 4.14.
Model A1 indicated that there is no significant relationship between the value-expressive function and the intention to purchase counterfeit products, leading to the conclusion that Hypothesis 1 (H1) is unsupported Interestingly, the data suggests that the influence of the value-expressive function on counterfeit purchase intention may be positive rather than negative, contrary to what was outlined in the literature review Consequently, both H1 and Hypothesis 3 (H3) require rephrasing to reflect these findings.
H1: Value-expressive function has a positive effect on counterfeit purchase intention.H3: High level of product involvement weakens the relationship between value- expressive function and counterfeit purchase intention.
Table 4.14: Hierarchical multiple regression – Model A
Coefficients R- square R-square change Sig
Product involvement Value-expressive function x
Figure 4.2: Moderation effect of product involvement on the relationship between value-expressive function and counterfeit purchase intention
The analysis of Model A2 and A3 revealed a significant increase in R-square by 024, indicating a meaningful relationship at the 05 level Additionally, the beta coefficient for the moderation predictor, which combines value-expressive function and counterfeit purchase intention, was significant, thereby supporting hypothesis H3 This moderation effect of product involvement on the connection between value-expressive function and counterfeit purchase intention is illustrated in Figure 4.2.
Model B was utilized to examine the influence of social-adjustive function on the intention to purchase counterfeit products (H2), alongside assessing the moderating role of product involvement in this relationship (H4) The findings from this regression analysis are presented below.
Table 4.15: Hierarchical multiple regression – Model B
Coefficients R- square R-square change Sig
Product involvement Social-adjustive function x
Model B1 demonstrated that the social-adjustive function positively influences the intention to purchase counterfeit products, confirming H2 However, Models B2 and B3 indicated no significant change in R-square values, and the beta coefficient for the moderation predictor (social-adjustive function x product involvement) was found to be insignificant, leading to the conclusion that H4 was not supported.
Model C was employed to examine how product knowledge moderates the relationship between social-adjustive function and the intention to purchase counterfeit products (H5) The findings of this regression analysis are presented in Table 4.16.
Table 4.16: Hierarchical multiple regression – Model C
Coefficients R- square R-square change Sig
Product knowledge Social-adjustive function x
The findings from Model C1 and C2 indicate a significant moderating effect of product knowledge on the relationship between social-adjustive function and the intention to purchase counterfeit products The change in R-square for Model C2 was significant, and the beta coefficient for the interaction between social-adjustive function and product knowledge was also significant Additionally, the model fit p-value for Model C2 was less than 001, supporting hypothesis H5.
Figure 4.3: Moderation effect of product knowledge on the relationship between social-adjustive function and counterfeit purchase intention
The moderation effect of product knowledge on the relationship between social- adjustive function and counterfeit purchase intention is demonstrated in Figure 4.3.
Discussion
The findings from Model A indicated that the value-expressive function has no significant impact on counterfeit purchase intention (β = 078; p = 271), thus failing to support H1 Interestingly, the effect was found to be positive rather than negative This outcome aligns with previous research by Wilcox et al (2009), which noted that the influence of the value-expressive function on counterfeit purchase intention varies based on consumer beliefs For instance, a consumer aiming to portray themselves as savvy may prefer counterfeits, even when brands emphasize value-expressive attributes.
According to Perez et al (2010), some consumers purchase counterfeit products to demonstrate their ability to outsmart others This suggests that brands aiming to emphasize intelligence and exclusivity may inadvertently provoke negative reactions from consumers.
The social-adjustive function significantly influences counterfeit purchase intention, with a beta coefficient of 248 and a p-value of less than 001, supporting hypothesis H2 Additionally, research by Wilcox et al (2009) indicates that the impact of social-adjustive function on counterfeit purchase intention is greater than that of value-adjustive function, further clarifying the findings of H2.
The study found a significant negative moderation effect of product involvement on the relationship between value-expressive function and counterfeit purchase intention (βvalue-expressive function x product involvement = -.519; p = 029; H3 supported) Specifically, high value-expressive function combined with high product involvement leads to a decreased intention to purchase counterfeits This supports the argument that heightened product involvement prompts consumers to more carefully consider the risks and self-deception associated with counterfeit purchases, ultimately diminishing any perceived benefits of buying such products.
The study found that product involvement did not significantly moderate the relationship between social-adjustive function and counterfeit purchase intention (βsocial-adjustive function x product involvement = -.299; p = 268; H4 not supported) Despite the negative interaction effect aligning with initial hypotheses, the lack of significance may stem from the oversimplified measurement of product involvement, which categorized it into high and low levels using a median-split approach, as suggested by Zaichkowsky.
In their research, McQuarrie and Munson (1992) highlighted the need for a detailed measurement of high involvement, suggesting that significant differences exist among various types of involvement They pointed out that products can vary in importance and interest; for instance, a product may be deemed important yet uninteresting, while another could be interesting but not crucial This distinction is crucial, as median-split categorization could inaccurately group these differing products together.
Therefore, the moderation effect might have been significant if detail categorize methods had been employed.
The findings revealed a significant negative relationship between the social-adjustive function and counterfeit purchase intention, influenced by product knowledge (β = -.601; p = 042), thus supporting hypothesis H5 This suggests that consumers with high product knowledge are more aware of the potential risks associated with counterfeit products, leading to a reduced intention to purchase them.
Summary
Following minor wording adjustments in the pilot study, the measurement scale underwent validation The findings indicated that item PK3 should be removed, resulting in a final scale comprising 24 items for the hypothesis tests.
The results of the hypothesis tests are demonstrated in Table 4.17.
H1* Value-expressive function has a positive effect on counterfeit purchase intention Not supported
H2 Social-adjustive function has a positive effect on counterfeit purchase intention Supported
H3* High level of product involvement weakens the relationship between value-expressive function and counterfeit purchase intention Supported
H4 High level of product involvement weakens the relationship between social-adjustive function and counterfeit purchase intention Not supported
H5 High level of product knowledge weakens the relationship between social-adjustive function and counterfeit purchase intention Supported
The study found strong support for H2, H3, and H5, while H1 and H4 were not supported Interestingly, although value-expressive function positively influenced counterfeit purchase intention, this effect was not significant Previous research has indicated the instability of the value-expressive function's impact on counterfeit purchasing, providing an explanation for the lack of support for H1.
The lack of support for H4 may stem from the oversimplification inherent in the median-split method McQuarrie and Munson (1992) recommended a more nuanced approach to understanding product involvement Additional research is necessary to validate the significance of H4.
CONCLUSION
Overview
Luxury brands are increasingly threatened by counterfeits, which significantly diminish their revenues and profits while devaluing their exclusivity This underscores the need for research into the motivations behind the intention to purchase counterfeit luxury items This study aims to explore how the psychological functions of products influence counterfeit purchase intentions, while also considering the moderating effects of product involvement and product knowledge, which are crucial in various marketing contexts.
The findings indicate that the social-adjustive function effect, along with the interplay between product involvement and value-expressive function, as well as the relationship between product knowledge and social-adjustive function, are significant These insights provide business managers with various strategies to mitigate demand-side counterfeit risks.
Managerial implications
The findings from H1 and H2 indicate that the social-adjustive function significantly influences counterfeit purchase intentions, whereas the value-expressive function does not show a significant effect This suggests that luxury products marketed as social-fit solutions, designed to help customers fit in during special occasions, are at a heightened risk of counterfeiting as this function becomes more recognized by consumers In contrast, luxury items promoted as self-expressive solutions, which encourage individuals to "be your true self," face fewer counterfeit risks This aligns with the insights of Wilcox et al (2009) and is confirmed in the context of Vietnamese consumers Interestingly, while the self-expressive value does not diminish, it actually increases the intention to purchase counterfeits, although this effect is deemed insignificant in the current study.
H3 suggests that products marketed as tools for self-expression must enhance customer engagement Essentially, items that promote authenticity should be presented as valuable and captivating, thereby reducing the likelihood of consumers opting for counterfeit alternatives.
Marketing campaigns aimed at increasing consumer involvement are not effective in reducing demand-side counterfeit risks when products are marketed as social-fit-in solutions Instead, brands that fit this category should focus on informative campaigns that educate customers about the product and its category By enhancing consumer knowledge, these brands can effectively lower the intention to purchase counterfeit products.
To mitigate the risks associated with counterfeiting, luxury brands should adopt two key marketing strategies: first, a value-expressive approach that integrates product involvement, and second, a social-adjustive strategy that emphasizes product knowledge Other combinations of these strategies have yet to demonstrate clear effectiveness.
Limitations and future research
While the research findings are well-supported by data, significant limitations exist The study was conducted solely in Ho Chi Minh City, Vietnam, with respondents exclusively from this region Given the variations in psychological functions across different cultures, the results may not be applicable to countries with distinct cultural backgrounds Future research should focus on comparing customer groups from diverse cultures and examine the moderating effects of demographic factors such as country and race.
The relationship between value-expressive function and counterfeit purchase intention is positively correlated, contrary to earlier studies, although the significance of this effect is limited This discrepancy may stem from cultural differences, as previous research predominantly focuses on Western cultures Consequently, further investigation is essential to validate this positive relationship, particularly within Vietnamese and broader Asian cultural contexts.
This study is limited to fashion-related luxury products and does not address other significant luxury categories, such as smartphones, furniture, or cars Consequently, the findings may not be relevant to other types of luxury items.
Product involvement plays a crucial moderating role in the relationship between value-expressive function and counterfeit purchase intention McQuarrie and Munson (1992) propose that product involvement is a higher-order construct, consisting of two key sub-constructs: product importance and product interest While some researchers classify products based on overall involvement levels—such as high, medium, and low (Zaichkowsky, 1994)—the framework established by McQuarrie and Munson offers a more nuanced understanding of product involvement.
Research from 1992 suggests that variations exist among high-involvement product groups, indicating distinct differences between products with high importance and low interest versus those with low importance and high interest Consequently, it is anticipated that these diverse groups of high-involvement products may exert varying impacts Therefore, studies exploring the distinctions among high-interest-low-importance, low-interest-high-importance, and high-interest-high-importance products, alongside comparisons to low-involvement products, are crucial for understanding their effects on psychological functions and counterfeit purchase intentions.
Future research is essential to enhance our understanding of consumer behavior across different cultures and the varying impacts of high-involvement products Gaining insights into these areas will enable the luxury-product industry to develop more effective strategies in combating counterfeiting.
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Counterfeit purchase intention (1 = completely disagree; 7 = completely agree)
There is a significant likelihood of purchasing counterfeit fashion-related luxury products, indicating a strong intention among consumers to acquire these imitations This trend reflects a growing acceptance and interest in counterfeit items within the luxury market.
Value-expressive function (1 = completely disagree; 7 = completely agree)
VE1: Luxury brand reflects the kind of person I see myself to be 1 2 3 4 5 6 7
VE2: Luxury brand helps me communicate my self-identity 1 2 3 4 5 6 7
VE3: Luxury brand helps me express myself 1 2 3 4 5 6 7
VE4: Luxury brand helps me define myself 1 2 3 4 5 6 7
Social-adjustive function (1 = completely disagree; 7 = completely agree)
SA1: Luxury brand is a symbol of social status 1 2 3 4 5 6 7
SA2: Luxury brand helps me fit into important social situations 1 2 3 4 5 6 7
SA3: I like to be seen wearing/using luxury brand 1 2 3 4 5 6 7
SA4: I enjoy it when people know I am wearing/using a luxury brand 1 2 3 4 5 6 7
Product knowledge (1 = completely disagree; 7 = completely agree)
PK1: I feel very knowledgeable about fashion products 1 2 3 4 5 6 7
PK2: If a friend asked me about fashion products, I could give them advice about different brands.
1 2 3 4 5 6 7 PK3: If I had to purchase fashion products today, I would need to gather very little information in order to make a wise decision 1 2 3 4 5 6 7
PK4: I feel very confident about my ability to tell the difference in quality among different brands of fashion products.
I consider fashion products to be:
PI3: Means nothing to me 1 2 3 4 5 6 7 Means a lot to me
PI6: Doesn‘t matter 1 2 3 4 5 6 7 Matter to me
PI10: Of no concern 1 2 3 4 5 6 7 Of concern to me
You are: Male/Female Have you ever bought a luxury product? Yes/No Ý định mua sản phẩm giả/nhái (1 = hoàn toàn không đồng ý; 7 = hoàn toàn đồng ý)
Nhiều khả năng tôi sẽ mua sản phẩm thời trang hàng hiệu giả/nhái Rất có thể tôi sẽ quyết định sở hữu những món đồ này Tôi có ý định rõ ràng trong việc lựa chọn sản phẩm thời trang hàng hiệu giả/nhái để phục vụ nhu cầu của mình.
Chức năng thể hiện giá trị (1 = hoàn toàn không đồng ý; 7 = hoàn toàn đồng ý)
VE1: Hàng hiệu phản ánh loại người mà tôi muốn trở thành 1 2 3 4 5 6 7
VE2: Hàng hiệu giúp tôi truyền đạt bản sắc cá nhân 1 2 3 4 5 6 7
VE3: Hàng hiệu giúp tôi thể hiện bản thân 1 2 3 4 5 6 7
VE4: Hàng hiệu giúp tôi định nghĩa bản thân 1 2 3 4 5 6 7
Chức năng điều chỉnh xã hội (1 = hoàn toàn không đồng ý; 7 = hoàn toàn đồng ý)
SA1: Hàng hiệu là biểu tượng cho địa vị xã hội 1 2 3 4 5 6 7
SA2: Hàng hiệu giúp tôi hòa nhập trong những tình huống xã hội quan trọng 1 2 3 4 5 6 7
SA3: Tôi thích được thấy đang mặc/sử dụng hàng hiệu 1 2 3 4 5 6 7
SA4: Tôi thích khi người khác biết rằng tôi đang mặc/sử dụng hàng hiệu 1 2 3 4 5 6 7
Hiểu biết về sản phẩm (1 = hoàn toàn không đồng ý; 7 = hoàn toàn đồng ý)
Tôi tự tin vào kiến thức của mình về sản phẩm thời trang và có thể tư vấn cho bạn bè về các thương hiệu khác nhau trong lĩnh vực này.
PK3: Nếu phải mua sản phẩm thời trang hôm nay, tôi sẽ cần thu thập rất ít thông tin để ra quyết định thông minh.
1 2 3 4 5 6 7 PK4: Tôi cảm thấy rất tự tin về khả năng nhận biết sự khác biệt về chất lượng giữa các thương hiệu sản phẩm thời trang khác nhau.
Sự gắn kết với sản phẩm
Tôi thấy sản phẩm thời trang:
PI1: Không quan trọng 1 2 3 4 5 6 7 Quan trọng
PI2: Không phù hợp 1 2 3 4 5 6 7 Phù hợp
PI3: Không có ý nghĩa với tôi 1 2 3 4 5 6 7 Rất có ý nghĩa với tôi
PI4: Không kích thích 1 2 3 4 5 6 7 Kích thích
PI5: Tẻ nhạt 1 2 3 4 5 6 7 Thanh nhã
PI6: Không ảnh hưởng tới tôi 1 2 3 4 5 6 7 Ảnh hưởng tới tôi
PI8: Không thú vị 1 2 3 4 5 6 7 Thú vị
PI9: Không thu hút 1 2 3 4 5 6 7 Thu hút
PI10: Không đáng quan tâm 1 2 3 4 5 6 7 Đáng quan tâm
Bạn là: Nam/Nữ Bạn đã từng mua hàng xa xỉ? Đã từng/Chưa từng
Total Correlation Cronbach's Alpha if Item Deleted
Total Correlation Cronbach's Alpha if Item Deleted
Total Correlation Cronbach's Alpha if Item Deleted
Total Correlation Cronbach's Alpha if Item Deleted
Product knowledge after deleting PK3
Total Correlation Cronbach's Alpha if Item Deleted
Total Correlation Cronbach's Alpha if Item Deleted
Total Correlation Cronbach's Alpha if Item Deleted
Kaiser-Meyer-Olkin Measure of Sampling Adequacy .868
Bartlett's Test of Sphericity Approx Chi-Square 3.270E3 df 276
Factor loading, Promax rotation with Kaiser Normalisation, k=4
Extraction Method: Principal Component Analysis
Rotation Method: Promax with Kaiser Normalization.
Estimate S.E C.R P Label SA1 < - social-adjustive function 1.000
Estimate VE1 < - value-expressive function 749
M.I Par Change M.I Par Change e24 < > CPI 5.968 0.305 e15 < > CPI 9.323 -0.411 e24 < > VEF 5.743 0.193 e15 < > e24 11.192 -0.284 e22 < > CPI 5.305 -0.263 e15 < > e21 7.233 -0.185 e22 < > PK 11.132 -0.279 e15 < > e20 19.3 0.348 e22 < > e23 64.906 0.575 e15 < > e19 6.129 -0.194 e21 < > CPI 4.828 0.223 e15 < > e17 38.327 0.547 e21 < > PK 4.022 -0.149 e13 < > e23 4.053 0.106 e21 < > e24 13.767 0.234 e12 < > e23 7.567 -0.152 e21 < > e23 6.871 -0.165 e11 < > SAF 7.617 0.223 e20 < > PK 4.314 0.177 e11 < > e14 5.918 0.294 e19 < > e24 7.068 0.192 e11 < > e13 4.019 -0.131 e19 < > e23 7.997 -0.203 e10 < > e22 6.632 -0.191 e19 < > e21 25.801 0.296 e10 < > e18 5.924 0.192 e19 < > e20 8.571 -0.198 e10 < > e16 5.667 0.201 e18 < > PK 10.256 0.285 e10 < > e13 5.203 0.124 e18 < > e22 9.155 -0.211 e9 < > e20 14.065 0.273 e17 < > CPI 4.37 -0.271 e8 < > e24 5.803 -0.202 e17 < > PK 4.88 0.21 e8 < > e11 6.026 0.253 e17 < > e24 11.867 -0.281 e7 < > VEF 4.45 -0.154 e17 < > e21 10.853 -0.218 e6 < > e19 6.051 0.191 e17 < > e20 7.691 0.212 e5 < > e24 4.057 0.203 e17 < > e19 5.405 -0.175 e5 < > e19 5.28 -0.213 e16 < > PK 4.751 0.208 e5 < > e8 4.1 -0.215 e16 < > e22 7.889 -0.211 e3 < > SAF 4.652 0.138 e16 < > e18 9.006 0.239 e1 < > e5 9.682 0.332
Model NPAR CMIN DF P CMIN/DF
Model RMR GFI AGFI PGFI
Model RMSEA LO 90 HI 90 PCLOSE
Model NPAR CMIN DF P CMIN/DF
Model RMR GFI AGFI PGFI
Model RMSEA LO 90 HI 90 PCLOSE
ValueExpressive SocialAdjustive ProductKnowled ge ProductInvolvement CounterfeitPurchas eIntention ValueExpressive Pearson
** Correlation is significant at the 0.01 level (2- tailed).
Square Std Error of the Estimate
Change F Change df1 df2 Sig F
3 173 c 030 015 1.61557 024 4.819 1 197 029 1.783 a Predictors: (Constant), ValueExpressive b Predictors: (Constant), ValueExpressive,
PIcategorized c Predictors: (Constant), ValueExpressive, PIcategorized, VExPI d Dependent Variable: CounterfeitPurchaseIntention
Model Sum of Squares df Mean Square F Sig.
529.991 200 a Predictors: (Constant), ValueExpressive b Predictors: (Constant), ValueExpressive, PIcategorized c Predictors: (Constant), ValueExpressive, PIcategorized, VExPI d Dependent Variable: CounterfeitPurchaseIntention
Square Std Error of the Estimate
Change F Change df1 df2 Sig F
3 262 c 069 054 1.58298 006 1.234 1 197 268 1.735 a Predictors: (Constant), SocialAdjustive b Predictors: (Constant), SocialAdjustive, PIcategorized c Predictors: (Constant), SocialAdjustive, PIcategorized, SAxPI d Dependent Variable: CounterfeitPurchaseIntention
Model Sum of Squares df Mean Square F Sig.
529.991 200 a Predictors: (Constant), SocialAdjustive b Predictors: (Constant), SocialAdjustive, PIcategorized c Predictors: (Constant), SocialAdjustive, PIcategorized, SAxPI d Dependent Variable: CounterfeitPurchaseIntention
Square Std Error of the Estimate
Change F Change df1 df2 Sig F
SocialAdjustive b Predictors: (Constant), PKcategorized, SocialAdjustive, SAxPK c Dependent Variable: CounterfeitPurchaseIntention
Model Sum of Squares df Mean Square F Sig.
529.991 200 a Predictors: (Constant), PKcategorized, SocialAdjustive b Predictors: (Constant), PKcategorized, SocialAdjustive, SAxPK c Dependent Variable: CounterfeitPurchaseIntention a Dependent Variable: CounterfeitPurchaseIntention